Ziv Bar‐Joseph

21.6k total citations · 4 hit papers
160 papers, 10.1k citations indexed

About

Ziv Bar‐Joseph is a scholar working on Molecular Biology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Ziv Bar‐Joseph has authored 160 papers receiving a total of 10.1k indexed citations (citations by other indexed papers that have themselves been cited), including 131 papers in Molecular Biology, 13 papers in Artificial Intelligence and 12 papers in Cancer Research. Recurrent topics in Ziv Bar‐Joseph's work include Bioinformatics and Genomic Networks (59 papers), Gene Regulatory Network Analysis (46 papers) and Single-cell and spatial transcriptomics (44 papers). Ziv Bar‐Joseph is often cited by papers focused on Bioinformatics and Genomic Networks (59 papers), Gene Regulatory Network Analysis (46 papers) and Single-cell and spatial transcriptomics (44 papers). Ziv Bar‐Joseph collaborates with scholars based in United States, Israel and Canada. Ziv Bar‐Joseph's co-authors include Jason Ernst, Itamar Simon, David K. Gifford, Tommi Jaakkola, Anthony Gitter, Judith Klein‐Seetharaman, Yanjun Qi, Ye Yuan, Gerard J. Nau and Guy Zinman and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Ziv Bar‐Joseph

151 papers receiving 9.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

STEM: a tool for the analysis of short time series gene expression data

2006 1.3k citations Ziv Bar‐Joseph et al. profile →
The sirtuin SIRT6 regulates lifespan in male mice

2012 800 citations Ziv Bar‐Joseph et al. profile →
A transcription factor hierarchy defines an environmental stress response network

2016 396 citations Liang Song, Shao‐shan Carol Huang et al. Science profile →
Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis

2013 334 citations Shao‐shan Carol Huang, Joseph R. Nery et al. profile →

Peers

Countries citing papers authored by Ziv Bar‐Joseph

Since Specialization

Citations

This map shows the geographic impact of Ziv Bar‐Joseph's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ziv Bar‐Joseph with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ziv Bar‐Joseph more than expected).

Fields of papers citing papers by Ziv Bar‐Joseph

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ziv Bar‐Joseph. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ziv Bar‐Joseph. The network helps show where Ziv Bar‐Joseph may publish in the future.

Co-authorship network of co-authors of Ziv Bar‐Joseph

This figure shows the co-authorship network connecting the top 25 collaborators of Ziv Bar‐Joseph. A scholar is included among the top collaborators of Ziv Bar‐Joseph based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ziv Bar‐Joseph. Ziv Bar‐Joseph is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	mRNA-LM: full-length integrated SLM for mRNA analysis 2025 ·Nucleic Acids Research ·(unknown), (unknown), (unknown), (unknown), (unknown), Dinghai Zheng, (unknown), Vikram Agarwal, Ziv Bar‐Joseph, Sven Jäger	3
2	Proxy endpoints — bridging clinical trials and real world data 2024 ·Journal of Biomedical Informatics ·(unknown), (unknown), (unknown), (unknown), (unknown), Flavio Dormont, (unknown), Ziv Bar‐Joseph, (unknown)	0
3	Recovering single-cell expression profiles from spatial transcriptomics with scResolve 2024 ·Cell Reports Methods ·Hao Chen, (unknown), (unknown), (unknown), Mauricio Rojas, Ana L. Mora, Ziv Bar‐Joseph, Jose Lugo-Martinez	3
4	Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework 2024 ·Genome Research ·Haotian Teng, Marcus H. Stoiber, Ziv Bar‐Joseph, (unknown)	7
5	Constrained Pseudo-Time Ordering for Clinical Transcriptomics Data 2024 ·IEEE/ACM Transactions on Computational Biology and Bioinformatics ·Sachin Mathur, Hamid Mattoo, Ziv Bar‐Joseph	0
6	SCS: cell segmentation for high-resolution spatial transcriptomics 2023 ·Nature Methods ·Hao Chen, Dongshunyi Li, Ziv Bar‐Joseph	37
7	Using single cell atlas data to reconstruct regulatory networks 2023 ·Nucleic Acids Research ·Qi Song, Matthew Ruffalo, Ziv Bar‐Joseph	7
8	De novo construction of T cell compartment in humanized mice engrafted with iPSC-derived thymus organoids 2022 ·Nature Methods ·Ann E. Zeleniak, (unknown), Wen Liu, (unknown), K. Ravikumar, Amir Alavi, (unknown), Suzanne Bertera, Robert Lakomy, Asako Tajima, Henry Cohen, Stephanie M. Wong, (unknown), (unknown), Ziv Bar‐Joseph, Massimo Trucco, Ipsita Banerjee, Yong Fan	43
9	Deep learning of gene relationships from single cell time-course expression data 2021 ·Briefings in Bioinformatics ·Ye Yuan, Ziv Bar‐Joseph	27
10	Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data 2021 ·mSystems ·Daniel Ruiz-Perez, Jose Lugo-Martinez, Natalia Bourguignon, Kalai Mathee, (unknown), Ziv Bar‐Joseph, Giri Narasimhan	37
11	Identifying signaling genes in spatial single-cell expression data 2020 ·Bioinformatics ·Dongshunyi Li, Jun Ding, Ziv Bar‐Joseph	34
12	Continuous-state HMMs for modeling time-series single-cell RNA-Seq data 2019 ·Bioinformatics ·Chieh Hubert Lin, Ziv Bar‐Joseph	29
13	Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data 2019 ·Bioinformatics ·Sabrina Mohd Rashid, Sohrab P. Shah, Ziv Bar‐Joseph, Ravi Pandya	34
14	Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains 2019 ·Proceedings of the National Academy of Sciences ·Sabrina Mohd Rashid, Zhicheng Long, Shashank Singh, (unknown), (unknown), Saket Navlakha, Hanna Salman, Zoltán N. Oltvai, Ziv Bar‐Joseph	14
15	A transcription factor hierarchy defines an environmental stress response network breakdown → 2016 ·Science ·Liang Song, Shao‐shan Carol Huang, Aaron Wise, Rosa Castanon, Joseph R. Nery, Huaming Chen, Marina Watanabe, (unknown), Ziv Bar‐Joseph, Joseph R. Ecker	396
16	Inferring Interaction Networks using the IBP applied to microRNA Target Prediction 2011 ·Neural Information Processing Systems ·Hai-Son Le, Ziv Bar‐Joseph	9
17	Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs 2011 ·Journal of Computational Biology ·(unknown), Ziv Bar‐Joseph, Robert F. Murphy	4
18	Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings 2010 ·Neural Information Processing Systems ·Ziv Bar‐Joseph, Hai-Son Le	2
19	Totally Ordered Multicast with Bounded Delays and Variable Rates. 2000 ·Ziv Bar‐Joseph, Idit Keidar, Tal Anker, Nancy Lynch	2
20	Granular Synthesis of Sound Textures Using Statistical Learning 1999 ·The Journal of the Abraham Lincoln Association ·Ziv Bar‐Joseph, Dani Lischinski, Michael Werman, Shlomo Dubnov, Ran El‐Yaniv	9

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact